User’s Guide to EMMIX: This document outlines the operation and the available options of the program EMMIX. Brief instructions on the form of the input and output files are also given. The main purpose of the program is to fit a mixture model of multivariate normal or t-distributed components to a given data set. This is approached by using maximum likelihood via the EM algorithm of Dempster, Laird, and Rubin (1977); for a full examination of the EM algorithm and related topics, see McLachlan and Krishnan (1997). Many other features are also included, that were found to be of use when fitting mixture models.

References in zbMATH (referenced in 20 articles , 1 standard article )

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  1. Andrews, Jeffrey L.: Addressing overfitting and underfitting in Gaussian model-based clustering (2018)
  2. Cinar, Ozan; Ilk, Ozlem; Iyigun, Cem: Clustering of short time-course gene expression data with dissimilar replicates (2018)
  3. Fop, Michael; Murphy, Thomas Brendan: Variable selection methods for model-based clustering (2018)
  4. Clarke, Brenton R.; Davidson, Thomas; Hammarstrand, Robert: A comparison of the (L_2) minimum distance estimator and the EM-algorithm when fitting (k)-component univariate normal mixtures (2017)
  5. Lin, Tsung-I; McLachlan, Geoffrey J.; Lee, Sharon X.: Extending mixtures of factor models using the restricted multivariate skew-normal distribution (2016)
  6. Bouveyron, Charles; Brunet-Saumard, Camille: Model-based clustering of high-dimensional data: a review (2014)
  7. Galimberti, Giuliano; Soffritti, Gabriele: Using conditional independence for parsimonious model-based Gaussian clustering (2013)
  8. Çalış, Nazif; Erol, Hamza: A new per-field classification method using mixture discriminant analysis (2012)
  9. Heinrich Fritz; Luis García-Escudero; Agustín Mayo-Iscar: tclust: An R Package for a Trimming Approach to Cluster Analysis (2012) not zbMATH
  10. Giannakopoulou, Dimitra; Bushnell, David H.; Schumann, Johann; Erzberger, Heinz; Heere, Karen: Formal testing for separation assurance (2011)
  11. Francis, Brian; Dittrich, Regina; Hatzinger, Reinhold: Modeling heterogeneity in ranked responses by nonparametric maximum likelihood: How do Europeans get their scientific knowledge? (2010)
  12. Marot, Guillemette; Mayer, Claus-Dieter: Sequential analysis for microarray data based on sensitivity and meta-analysis (2009)
  13. Nadarajah, Saralees; Kotz, Samuel: Estimation methods for the multivariate (t) distribution (2008)
  14. McLachlan, G. J.; Khan, N.: On a resampling approach for tests on the number of clusters with mixture model-based clustering of tissue samples (2004)
  15. Hardin, Johanna; Rocke, David M.: Outlier detection in the multiple cluster setting using the minimum covariance determinant estimator (2003)
  16. Parmigiani, Giovanni (ed.); Garrett, Elizabeth S. (ed.); Irizarry, Rafael A. (ed.); Zeger, Scott L. (ed.): The analysis of gene expression data. Methods and software (2003)
  17. Agusta, Yudi; Dowe, David L.: MML clustering of continuous-valued data using Gaussian and (t) distributions (2002)
  18. Fraley, Chris; Raftery, Adrian E.: Model-based clustering, discriminant analysis, and density estimation. (2002)
  19. Hennig, Christian: Fixed point clusters for linear regression: Computation and comparison (2002)
  20. Geoff McLachlan; David Peel: The EMMIX Algorithm for the Fitting of Normal and t-Components (1999) not zbMATH